Nonparametric Estimation of Conditional CDF and Quantile Functions With Mixed Categorical and Continuous Data
提出一种新的非参数条件累积分布函数核估计量，可处理混合类型数据，并基于条件概率密度函数选择带宽，自动剔除无关变量，理论性质良好，模拟和实例表现优于同类方法。
We propose a new nonparametric conditional cumulative distribution function kernel estimator that admits a mix of discrete and categorical data along with an associated nonparametric conditional quantile estimator. Bandwidth selection for kernel quantile regression remains an open topic of research. We employ a conditional probability density function-based bandwidth selector proposed by Hall, Racine, and Li that can automatically remove irrelevant variables and has impressive performance in this setting. We provide theoretical underpinnings including rates of convergence and limiting distributions. Simulations demonstrate that this approach performs quite well relative to its peers; two illustrative examples serve to underscore its value in applied settings.